# -*- coding: utf-8 -*-
"""
Created on Tue Jun  6 18:52:37 2017

@author: 代码医生 qq群：40016981，公众号：xiangyuejiqiren
@blog：http://blog.csdn.net/lijin6249
"""

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


plotdata = { "batchsize":[], "loss":[] }
def moving_average(a, w=10):
    if len(a) < w: 
        return a[:]    
    return [val if idx < w else sum(a[(idx-w):idx])/w for idx, val in enumerate(a)]


#生成模拟数据
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3 # y=2x，但是加入了噪声
#图形显示
plt.plot(train_X, train_Y, "ro", label="Original data")
plt.legend()
plt.show()




# 创建模型
# 占位符

inputdict = {
    "x": tf.placeholder("float"),
    "y": tf.placeholder("float")
}


# 模型参数
W = tf.Variable(tf.random_normal([1]), name="weight")
b = tf.Variable(tf.zeros([1]), name="bias")
# 前向结构
z = tf.multiply(inputdict["x"], W)+ b

#反向优化
cost =tf.reduce_mean( tf.square(inputdict["y"] - z))
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent

# 初始化变量
init = tf.global_variables_initializer()
#参数设置
training_epochs = 20
display_step = 2

# 启动session
with tf.Session() as sess:
    sess.run(init)

    # Fit all training data
    for epoch in range(training_epochs):
        for (x, y) in zip(train_X, train_Y):
            sess.run(optimizer, feed_dict={inputdict["x"]: x, inputdict["y"]: y})

        #显示训练中的详细信息
        if epoch % display_step == 0:
            loss = sess.run(cost, feed_dict={inputdict["x"]: train_X, inputdict["y"]:train_Y})
            print ("Epoch:", epoch+1, "cost=", loss,"W=", sess.run(W), "b=", sess.run(b))
            if not (loss == "NA" ):
                plotdata["batchsize"].append(epoch)
                plotdata["loss"].append(loss)

    print (" Finished!")
    print ("cost=", sess.run(cost, feed_dict={inputdict["x"]: train_X, inputdict["y"]: train_Y}), "W=", sess.run(W), "b=", sess.run(b))

    #图形显示
    plt.plot(train_X, train_Y, "ro", label="Original data")
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label="Fitted line")
    plt.legend()
    plt.show()
    
    plotdata["avgloss"] = moving_average(plotdata["loss"])
    plt.figure(1)
    plt.subplot(211)
    plt.plot(plotdata["batchsize"], plotdata["avgloss"], "b--")
    plt.xlabel("Minibatch number")
    plt.ylabel("Loss")
    plt.title("Minibatch run vs. Training loss")
     
    plt.show()

    print ("x=0.2，z=", sess.run(z, feed_dict={inputdict["x"]: 0.2}))

